title: Quantitative prediction of peptide binding to HLA-DP1 protein.
abstract: The exogenous proteins are processed by the host antigen-processing cells. Peptidic fragments of them are presented on the cell surface bound to the major hystocompatibility complex (MHC) molecules class II and recognized by the CD4+ T lymphocytes. The MHC binding is considered as the crucial prerequisite for T-cell recognition. Only peptides able to form stable complexes with the MHC proteins are recognized by the T-cells. These peptides are known as T-cell epitopes. All T-cell epitopes are MHC binders, but not all MHC binders are T-cell epitopes. The T-cell epitope prediction is one of the main priorities of immunoinformatics. In the present study, three chemometric techniques are combined to derive a model for in silico prediction of peptide binding to the human MHC class II protein HLA-DP1. The structures of a set of known peptide binders are described by amino acid z-descriptors. Data are processed by an iterative self-consisted algorithm using the method of partial least squares, and a quantitative matrix (QM) for peptide binding prediction to HLA-DP1 is derived. The QM is validated by two sets of proteins and showed an average accuracy of 86 percent.
authors.affiliation: Medical University of Sofia, Sofia.
authors.forename: Stefan
authors.initials: S
authors.lastname: Ivanov
authors.forename: Ivan
authors.initials: I
authors.lastname: Dimitrov
authors.forename: Irini
authors.initials: I
authors.lastname: Doytchinova
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D059868:HLA-DP beta-Chains / Q000737:chemistry* / Q000378:metabolism*; D006801:Humans; D008958:Models, Molecular; D015233:Models, Statistical; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results
chemical_list: D059868:HLA-DP beta-Chains; C054111:HLA-DPw1 antigen; D010455:Peptides